Near optimal control policy for controlling power system stabilizers using reinforcement learning

Novel hybrid arithmetic-equilibrium optimizer as robust tuner for power system stabilizer (PSS) parameters in interconnected electrical power system

A single machine-infinite-bus (SMIB) system including the interline power flow controllers (IPFCs) and the power system stabilizer (PSS) controller is addressed. The linearized system model is considered for investigating the interactions among IPFC and PSS controllers. To improve the stability of whole system again different disturbances, a lead-lag controller is considered to produce supplementary signal. The proposed supplementary controller is implemented to improve the damping of the power system low frequency oscillations (LFOs). Imperialist optimization algorithm (ICA) and shuffled frog leaping algorithm (SFLA) are implemented to search for optimal supplementary controllers and PSS parameters. Moreover, singular value decomposition (SVD) method is utilized to select the most effective damping control signal of IPFC lead-lag controllers. To evaluate the system performance, different operating conditions are considered. Reponses of system in five modes including uncoordinated and coordinated modes of IPFC and PSS using ICA and SFLA are studied and compared. Considering the results, response of system without controller shows the highest overshoot and the longest settling time for rotor angel at the different operating conditions. In this mode of system, rotor speed has the highest overshoot. Rotor angel in the system with only PSS includes lower overshoot and oscillation than system without controller. When PSS is only implemented, rotor speed deviation has the longest settling time. Rotor speed deviation in the uncoordinated mode of IPFC and PSS shows lower overshoot than system with only PSS and without controller. It is noticeable that in this mode, rotor angel has higher overshoot than system with only PSS. The superiority of the suggested ICA-based coordinated controllers is obvious compared with SFLA-based coordinated controllers and other system modes. Responses of coordinated PSS and IPFC SFLA-based supplementary controllers include higher peak amplitude and longer settling time compared with coordinated IPFC and PSS ICA-based controllers. This comparison shows that overshoots, undershoots and the settling times are reduced considerably in coordinated mode of IPFC based controller and PSS using ICA. Analysis of the system performance shows that the proposed method has excellent response to different faults in power system.

The main purpose of this paper is to propose an approach of the PSS (Power System Stabilizer) parameter tuning for interconnection system between Kalimantan Selatan - Tengah (Kalselteng) and Kalimantan Timur (Kaltim) in Indonesia. At this time, PSS module that exists on the generators in both of the subsystems have not been activated yet. This paper is expected to be a reference to determine the location and to tune the PSS parameter values for the other electrical power system in Indonesia. The location of PSS is determined by participation factor using DIgSILENT PowerFactory ® software, then the tuning is processed using Particle Swarm Optimization (PSO) method by MATLAB ®. After getting the tuned PSS parameter values, the response of the system towards the short-circuit simulation is compared with typical values of PSS parameter [7] as the validation process.

An efficient and most famous tool to enhance damping of the power system low frequency oscillations is the conventional widely used lead-lag Power System Stabilizer (PSS). To achieve the desired level of robust performance under transient situation, selecting a suitable design method for optimal tuning of PSS parameters is very important in multi-machine power system. Because, it is a multimodal and difficult combinatorial optimization problem, this paper presents a novel parameter automation strategy for Particle Swarm Optimization (PSO) called PSO with Time-Varying Acceleration Coefficients (PSO-TVAC). This optimization method has a strong ability to successfully control both global and local search in each iteration process for considerably increasing the probability of finding the global optimum solution. The PSO-TVAC algorithm is applied to optimal tuning PSS parameters problem in order to reduce the PSS design effort and find the best possible solution within a reasonable computation time. For this reason, the robustly selection of PSSs parameters is converted as an optimization problem based on the time domain-based objective function under different operating conditions. The robustness of the proposed method is demonstrated on a multi-machine power system in comparison with the classical PSO and conventional method based designed PSSs. It is shown through the nonlinear time domain simulation and some performance indices for a wide range of loading condition. The analysis of the results shows that the improved PSO-TVAC is not only very effective but also provides an excellent ability for damping low frequency oscillations and greatly enhance the dynamic stability of the power system. Moreover, the proposed PSO-TVAC algorithm is superior than that of the classical PSO one in terms of accuracy, convergence and computational effort. Key words: Power system stabilizer (PSS) design, particle swarm optimization, power system dynamics, time-varying acceleration coefficients.

The PSS (Power System Stabilizer) is a low cost method to stabilize the inter-tie and local oscillations of power network. The PSS's parameters are usually adjusted for the one-machine infinite-bus system model. On the contrary, the real power systems consist of multi machine systems. Then, the PSS parameters coordinated setting considering the real characteristics of the power system network is expected to improve the stability. For this purpose, the MPM (Modal Performance Measure) method is applied for adjusting PSS parameters. The power network mathematical model that is required for this calculation is given by the system identification method. Utilizing the MPM method characteristics, the restrictions such as damping coefficient and maximum gain that are practical design specifications used for conventional controllers, are naturally introduced. Furthermore, RBF (Radial Based Function) frequency weighing is introduced for adjusting frequency responses. The favorable results as expected are obtained by the large scale power system simulations.

Ensuring the transient stability of the power system networks is one of the prime challenges in the highly interconnected power systems. Though the low-frequency oscillations are not very harmful initially, failure to damping out may lead the system to go out of synchronism. The employment of flexible AC transmission systems (FACTS) devices may suppress these oscillations effectively in addition to the enhancement of power transfer capability. Among many FACTS devices, unified power flow controller (UPFC) is one of the most sophisticated ones. Tuning the parameters of power system stabilizer (PSS) coordinated with UPFC for a stable system is a multi-objective optimization problem. This paper aims to optimize the parameters of power system stabilizer (PSS) of power network incorporating UPFC using support vector regression (SVR) in real time to damp out the small signal oscillations hence to enhance the transient stability. System eigenvalues obtained from SVR tuned UPFC coordinated PSS and the fixed gain conventional PSS are compared to investigate the efficacy of the proposed technique for different loading conditions. Besides, time domain simulation comparison proves the superiority of the proposed technique over the conventional one. Furthermore, the statistical performance measures for training and testing datasets provide confidence on the developed SVR model.

In power system, the application of the power system stabiliser (PSS) has been proved to overcome the stability of power system. It is necessary to determine the appropriate PSS parameters. In this paper, a new cost function is presented for the tuning of PSS parameters. Optimisation of PSS parameters has been done by particle swarm optimisation algorithm. The power plant system that is simulated in this paper is one machine to infinite bus system. The responses of the PSS tuned with PSO and conventional power system stabiliser (CPSS) are compared with together.

In this paper, the Cuckoo Search Algorithm (CSA) is implemented on ten-machine thirty-nine bus New England power system for optimal tuning of Power System Stabilizer (PSS) parameters. The results are compared with Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSOA). For this, the design problem is formulated using an eigenvalue based multiobjective function that simultaneously control the damping ratio and damping factor for designing of PSS parameters. The optimized CSA based PSS (CSAPSS) parameters are used to shift the unstable or poorly damped electromechanical modes for severe line outage to a D-shape region in the left-half of the s-plane. The eigenvalue analysis, time-domain simulations and performances indices are used to observe the effectiveness of CSAPSS. The robustness of CSAPSS is compared with GA based PSS (GAPSS) and PSOA based PSS (PSOAPSS) under severe disturbances.

Changes in load on the power system suddenly, can cause dynamic disruption. This disturbance can not be responded well by the generator, so it can affect the system dynamic stability, such as the occurrence of oscillation speed and rotor angle. Conventional control of excitation and governor, also unable to repair the oscillations, so that additional controllers such as Power System Stabilizer (PSS) are required. In the use of PSS, there are several problems that often arise, namely the correct tuning of PSS parameters. In this research, we proposed a method of smart computing based on bat algorithm, for tuning PSS parameters. From the analysis results can be concluded, the performance performance of generator barru increased with the installation of Power System Stabilizer with optimal PSS parameter, with parameters respectively Kpss = 44.0828, T1 = 0.0284, T2 = 0.0146, T3 = 0.7818, T4 = 1.2816.

This paper reports a new technique for achieving optimized design for power system stabilizers. In any large scale interconnected systems, disturbances of small magnitudes are very common and low frequency oscillations pose a major problem. Hence small signal stability analysis is very important for analyzing system stability and performance. Power System Stabilizers (PSS) are used in these large interconnected systems for damping out low-frequency oscillations by providing auxiliary control signals to the generator excitation input. In this paper, collective decision optimization (CDO) algorithm, a meta-heuristic approach based on the decision making approach of human beings, has been applied for the optimal design of PSS. PSS parameters are tuned for the objective function, involving eigenvalues and damping ratios of the lightly damped electromechanical modes over a wide range of operating conditions. Also, optimal locations for PSS placement have been derived. Comparative study of the results obtained using CDO with those of grey wolf optimizer (GWO), differential Evolution (DE), Whale Optimization Algorithm (WOA) and crow search algorithm (CSA) methods, established the robustness of the algorithm in designing PSS under different operating conditions.

Due to the rising use of renewable energy sources and the use of contemporary power electronic equipment, power system stability has become a major challenge in current power systems. Controlling the power system characteristics can increase the stability of the power system. The traditional techniques for improving power system stability, such as the use of FACTS devices, are costly and may not be effective in handling the dynamic changes of the power system. As a result, by optimizing the power system parameters, an optimization-based multi-agent system can improve the stability of the power system. The Grey Wolf Optimizer based Multi Agent System (GWO-MAS) is proposed in this paper to improve power system stability. The control agents of Distributed Generations (DGs) are optimized using GWO algorithm based on the deviations in frequencies of DGs. Particle Swarm Optimization based Multi Agent System (PSO-MAS) and Conventional Multi Agent System performance are compared with the suggested technique's implementation on IEEE 30 bus system and testing in MATLAB/Simulink environment. The outcomes showed that the suggested approach is successfully improving the stability of an interconnected power system.

In power system, the application of power system stabilizer (PSS) has been proved to overcome the stability of power system. But on the certain condition such as full load, it is necessary to determine the appropriate PSS parameter. This paper presents the application of algorithm genetic for tuning power system parameter stabilizer. The power plant system that is simulated in this paper is the model of Suralaya Power Generation in West Java - Indonesia. The effectiveness of this method is shown in eigen value analysis and the result of response system simulation.

In this paper, a method for designing coordinated power system stabilizer (PSS) is presented. The proposed method is based on the reference model controller design. To this end, a model that is called single-machine non-infinite bus is presented for studying low frequency oscillations. Then a reference model is proposed for designing PSS based on the single-machine non-infinite bus model. Transfer functions of the understudy machine augmented with stabilizer are equated by transfer functions of the proposed reference model to compute the PSS's parameters. The damping of the reference model and consequently the damping of the designed PSS do not depend on the frequency of oscillations. Hence, the proposed PSS damps all intra- and inter-area oscillations and no incoordination will happen among the system's stabilizers. Two PSSs are designed using the proposed method for a test power system. To demonstrate the efficiency of the proposed method, the performance of the proposed PSSs are compared with the performance of multi-band PSS4B, conventional $\Delta \omega$ PSS, conventional $\Delta P_a$ PSS, and PSS designed based on particle swarm optimization. Robustness of stabilizers against variation of oscillations' frequency, variation of operating point, and existence of harmonics are used to measure their performances.

In this paper, Cuckoo Search Optimization (CSO) algorithm is presented for robust tuning of Power System Stabilizer (PSS) parameters in multimachine power system. In this work, a conventional speed-based lead-lag PSS is used. A damping ratio based objective function is formulated to optimize the damping of all electromechanical modes. The parameters of the CSO based PSS (CSOPSS) are optimized to shift the unstable or poorly damped eigenvalues associated with all electromechanical modes to a wedge-shape zone in left half of the s-plane. The eigenvalue analysis, time domain simulation results and performance indices show the effectives of CSOPSS by exhibiting better damping ratio than Genetic Algorithm based PSS (GAPSS) and Particle Swarm Optimization based PSO (PSOPSS). It is observed that CSOPSS exhibits improved damping performance compared to GAPSS and PSOPSS. The performance of proposed PSS is tested on Kundur 2-area 4-machine power system for wide range of operating conditions and severe disturbances.

Damping of electro-mechanical oscillations in interconnected power systems is important to guarantee secure and stable operation of the system. Power system stabilizers (PSSs) are applied to damp these oscillations. To overcome flows associated with conventional algorithm of PSS design, a modified version of non-dominated sorting genetic algorithm is used to regulate the PSS parameters in this paper. Additionally, fuzzy logic principle is employed to develop PSS in order to improve the stability and reliability of the power systems subjected to disturbances. Two-area (four-machine) power system is considered as the case study in this paper. PSS parameters are obtained for four PSS connected to four generators. The effectiveness of the proposed algorithm in damping the system oscillations during overall disturbances is evaluated. The simulation results illustrate this issue.